WHAT YOU CAN’T MEASURE - YOU CAN’T IMPROVE - The role of maturity models to improve data governance

Detta är en Master-uppsats från Göteborgs universitet/Graduate School

Sammanfattning: Background and purpose: As a consequence of the growing power of data, there is a needfor companies to maximise the value derived from it. However, to maximise the value derivedfrom data, it needs to be available, secure, relevant, and of high quality, which can be assuredby data governance. In addition, data governance has become crucial for companies to meetlegal requirements and to be competitive. The increasing need for data governance putspressure on organisations to control how they work with data and thus a need to improve. Tounderstand how an organisation works today and what can be improved, a maturity model canbe used. However, available data governance maturity models do not only miss out on aspectswithin data governance but also on how to use the model. Thus, the purpose of this study is toexplore how a maturity model can support organisations in improving data governance. Themodel is practically contributing as a tool for companies to assess their current level of maturityand to identify potential improvements.Methodology: A qualitative research strategy has been used throughout this study. Afterinvestigating existing literature, workshops with data governance experts were conducted.Based on the findings from literature and workshops, aspects important when creating themodel could be identified and the TMT Data Governance Maturity model was created. To testthe validity of the model and to determine what to take into consideration when using themodel, it was applied to a case company where semi-structured interviews with employeeswere conducted. The findings from the interviews were analysed by comparing the answers tothe levels in the model, using a thematic approach. The levels of maturity were then determinedbased on the average of all respondents' answers. By comparing the assigned levels with thehigher levels, actions for how to improve were identified and relevant improvement areas couldthereafter be defined.Main Findings: Based on the theoretical framework and workshops 13 elements wereidentified as crucial for data governance maturity models: Strategy & Approach, Leadership,Structure, Progress Measure, Knowledge & Change Management, Rules, Data Quality, DataSecurity & Privacy, Data Lifecycle Management, Metadata Management, Master DataManagement, Business Intelligence, and Adherence. The research also showed that animportant aspect of maturity models is interview questions reflecting the elements and somesort of measurement, which resulted in five levels being defined: Unaware, Ad Hoc, Proactive,Managed, and Optimised. When testing the model, one finding was that the model alwaysneeds to be adapted to each specific organisation before use to be of value, since all companiesare unique. If adapting the model to be in line with the characteristics of the organisation, thecurrent maturity level could be determined and thereby also what is needed to reach the higherlevels by identification of the gap. However, the result from using the maturity model onlyworks as guidance for what could be improved since the reality usually is more complex thanassigning an organisation a level on a scale.

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